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Track changes in Pied flycatchersOuwehand, Jacoba

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Chapter 6 African departure rather than migration

speed determines variation in spring

arrival in pied flycatchers

Janne Ouwehand and Christiaan Both

Journal of Animal Ecology, provisionally accepted

Chapter 6

134

Abstract

Properly timed spring migration enhances reproduction and survival. Climate

change requires organisms to respond to changes such as advanced spring

phenology. Pied flycatchers Ficedula hypoleuca have become a model species to

study such phenological adaptations of long-distance migratory songbirds to

climate change, but data on individuals’ time schedules outside the breeding

season are still lacking. Using light-level geolocators we studied variation in

migration schedules across the year in a pied flycatcher population in the

Netherlands, which sheds light on the ability for individual adjustments in spring

arrival timing to track environmental changes at their breeding grounds. We show

that variation in arrival dates to breeding sites in 2014 was caused by variation in

departure date from Sub-Saharan Africa, and not by environmental conditions

encountered en route. Spring migration duration was short for all individuals, on

average two weeks. Males migrated ahead of females in spring, while migration

schedules in autumn were flexibly adjusted according to breeding duties.

Individuals were therefore not consistently early or late throughout the year. In fast

migrants like our Dutch pied flycatchers, advancement of arrival to climate change

likely requires changes in spring departure dates. Adaptation for earlier arrival

may be slowed down by harsh circumstances in winter, or years with high costs

associated with early migration.

African departure determines variation in spring arrival

135

Introduction

Migration is an adaptive response to seasonally changing resources. Migrants

profit from peaks in food abundance at their temperate breeding grounds, but avoid

harsh conditions in winter (Alerstam, Hedenström & Åkesson 2003). Proper timing

is considered a key element in the migratory life style. An early arrival at breeding

sites enhances an individual’s chance to obtain a high quality territory and mate

(Lundberg & Alatalo 1992; Kokko 1999), which intensifies selection for timely

and fast spring migration (Alerstam 2011; Nilsson, Klaassen & Alerstam 2013).

Migrating too early can entail considerable costs of mortality when birds encounter

adverse weather or poor food supply upon arrival (Newton 2007). This intense

selection on pre-breeding timing is expected to reduce variation in spring timing

among birds, while post-breeding events are expected to be more variable

(McNamara, Welham & Houston 1998). In addition to natural drivers of selection,

human induced changes in the environment do impose additional and increasingly

important selection pressures. Afro-Palearctic migrants currently face rapid,

ongoing environmental changes at their wintering grounds (Vickery, Ewing &

Smith 2014), and also at their breeding grounds where the timing of peak food

abundance advances as result of climate change (Both et al. 2009). It is yet unclear

how well complex migratory life-cycles are suited to adapt to such changing, and

potentially less predictable, environments (Knudsen et al. 2011).

Pied flycatchers Ficedula hypoleuca have become a model species to study life-

cycle adaptation of long-distance migrants to climate change, with an emphasis on

how climate warming perturbs existing phenological adaptations at the breeding

grounds (Møller, Fiedler & Berthold 2010). Long-term data from 25 European

populations showed that flycatchers had the strongest advancements in laying dates

in areas with most spring warming (Both et al. 2004). Observed responses have

often been explained as phenotypic plasticity, with birds incorporating local

environmental conditions into migration and breeding decisions upon approaching

or after arrival at their breeding sites (Ahola et al. 2004; Both et al. 2004; Both,

Bijlsma & Visser 2005; Both and te Marvelde 2007; Hüppop & Winkel 2006;

Lehikoinen et al. 2004). Breeding ground studies also posed various claims about

the underlying mechanisms and ability of migrants to alter their migration

schedules to climate change without much data on individual time schedules

(Knudsen et al. 2011), particularly outside the breeding season. A lack of change

in spring arrival was interpreted as inflexibility associated with the mechanism

controlling migration departure from the wintering grounds (Both & Visser 2001),

while a later study, suggested that temperature constraints during migration

uncoupled spring departure from arrival at the breeding grounds (Both 2010).

Our limited knowlegde on how flexible individual migration schedules are for

free-living flycatchers comes from breeding ground arrival dates. Pied flycatchers

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136

in Spain and the Netherlands exhibit moderate repeatability in arrival dates,

showing that timing is consistently different among individuals (Both, Bijlsma &

Ouwehand 2016; Potti 1999 females only). This repeatability may hint at a

heritability of innate, rigid difference in migration timing as found in laboratory

studies (Gwinner 1996b). Alternatively, consistency in timing can arise from

reversible state effects that accumulate over an individual’s life (Senner, Conklin

& Piersma 2015). The latter has been described in American redstarts: early arrival

at high quality wintering sites advanced the timing of migratory departure in spring

and, subsequently led to earlier arrival timing and higher reproductive success

(Marra, Hobson & Holmes 1998; Norris et al. 2004), which may subsequently

carry-over to earlier autumn migration.

Despite pied flycatchers being one of the best studied migrant species in

relation to climate change, data on individuals’ time schedules and decisions in the

wild outside the breeding season are still lacking. Hence it remains an open

question as to what degree the observed variation and changes in arrival dates are

driven by individual adjustments in migration duration or departure decisions

(Tarka, Hansson, & Hasselquist 2015), or genetic adaptation in time schedules

(Jonzen et al. 2006). Here we make the crucial step by extending our

understanding to phases prior to arrival at the breeding sites, because migrants’

ability to adjust life-cycles to environmental change will depend on the constraints

and response modes of all traits involved (Botero et al. 2015), including those

during the migration phase.

In this paper we aim to describe determinants of arrival date at the breeding

grounds and their relation to preceding annual cycle events in the long-distance

migratory pied flycatcher (hereafter, ‘flycatcher’) using light-level geolocators

(hereafter, ‘geolocators’). Tracking studies in several species showed that variation

in timing of arrival within breeding populations is mainly determined by wintering

departure (e.g. Callo, Morton & Stutchbury 2013; Jahn et al. 2013; Lemke et al.

2013; Tøttrup et al. 2012b). Strong correlations among timing events in spring may

be expected if individual differences in migration schedules are rigid, and the

pressure for early arrival at breeding sites is strong. If strong selection, however,

reduced the variation in spring migration strategies among individuals, these

correlations are likely weaker. Furthermore, studies looking at within-individual

changes in other passerines showed that birds adjusted their spring departure

(Studds & Marra 2011) or arrival timing (Balbontin et al. 2009) to external

conditions in winter and during migration. Such fine-tuning to conditions along the

migration routes, also proposed in pied flycatchers (e.g. Ahola et al. 2004; Both

2010), can thereby disrupt the predicted strong correlation among timing events in

spring (Marra et al. 2005; Both 2010).

Migratory life-cycles as we observe them will therefore not only depend on the

underlying mechanisms, but also on an individuals’ ability to behave accordingly.

Individual differences in quality, condition and experience (Kokko 1999; Sergio et

African departure determines variation in spring arrival

137

al. 2014; McKinnon et al. 2014) may further mediate migratory performance and

payoffs associated with specific migratory timing, meaning that individuals may

adjust their migration decisions in response to intrinsic as well as external

variables. Such ‘mediated performance’ was found for Spanish pied flycatchers

where age and age-independent variation in wing length were correlated with male

arrival dates (Potti 1998). In such circumstances, variation in spring migration

schedules due to differences in endogenous program or cue-responses to

photoperiod (e.g. Maggini & Bairlein 2012; Gwinner 1996b) may not become

visible in arrival dates: i.e. the effect of variation in wing length and age on

migration speed may override the underlying time schedules and hence determines

individual variation in arrival timing (Potti 1998).

In this paper we examine if differences in timing between individuals persist or

change over the course of a year by studying i) correlations between sets of annual

cycle events, and, ii) changes in population variability in timing over the course of

the year. We specifically examine if differences among birds in sex and breeding

status, or breeding phenology contribute to the observed variation in migratory

schedules. Because differences in wintering site location (here, longitude) have the

potential to contribute to variation in timing, as recently shown between flycatcher

populations (Ouwehand et al. 2016), we also test if differences in wintering

longitude within our Dutch breeding population are correlated with timing of

annual cycle events.

Tracking studies are a great tool to describe individual differences in avian

migrants, but geolocator attachment may also mediate their performance (e.g.

Costantini & Møller 2013), and thereby hamper reliable inferences about natural

migration behaviour. We therefore first investigated geolocator and harness impact

on survival and timing of arrival and egg laying.

With this study we shed light on the role that individual adjustments during the

migration period have in allowing long-distance migrants’ to successfully track

environmental changes.

Materials and methods

Study system and field observations

Timing of migration in flycatchers was studied in 2013-2014 using field and

geolocator data from a nest box population established in 2007 in the

Dwingelderveld, the Netherlands (52°49’ N, 6°22’ E). The area consists of 12 plots

located in forest patches dominated by oak, pine or mixed forest, with each 50 or

100 nest boxes (approx. 50 m apart). This population has roughly 300 breeding

pairs, and an early breeding phenology compared to other European populations

(Both & te Marvelde 2007). Between years, 5-20% of males that occupy a territory

failed to attract a female (Both unpubl data), hereafter referred to as ‘unpaired

males’.

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Individuals were generally captured and ringed in the nest box halfway incubation

(females) or when feeding 7-d old chicks (males, females). Males still unmated in

mid-May were caught during nest box advertisement or using mist nets. The age of

unringed immigrants was estimated using feather characteristics, while age is

known for locally hatched birds. Sex was determined from plumage characteristics

and the presence of a brood patch. We visually monitored the spring arrival at least

every other day from April until mid-May: based on territorial behaviour (males)

or pair dates (females). Male and female identities as inferred from these

observations were checked and confirmed when captured later in the season (for

more details, see Both, Bijlsma & Ouwehand 2016). Newly built nests were

monitored at least every other day to determine the onset of egg laying. Actual

hatching dates were defined by daily nest checks around predicted hatching dates.

Geolocators

In 2013, 100 adults were equipped with Intigeo-W50 geolocators without light-

stalk (Migrate Technology Ltd, Cambridge, UK). Breeding birds (n = 28 females,

n = 55 males) were tagged prior to chick fledging, (chick age: 6-15 d). We

deployed the remaining geolocators on all available unpaired males still present at

20 May (n = 17). Most devices were attached using leg-loop harnesses, hereafter

‘LL’ (28 females, 52 males; following Rappole & Tipton 1991), but we also

equipped 20 breeding males with full-body harnesses consisting of one loop

around the neck and one around each wing, thus placing the device somewhat

higher on the bird’s back. A possible advantage of a FB harness could therefore be

that it places the tracking device closer to the gravity centre of the flying bird. We

performed this pilot study with full-body (FB) harnesses to test if they reduce the

impact of geolocators on birds behaviour and performance. A FB harness may

potentially increased drag (Bowlin et al. 2010), but field studies using them have

not found negative effects (e.g. Åkesson et al. 2012). Body mass at the time of

logger deployment was between 11.2 - 13.7 g (mean = 12.3 g, n = 98); geolocators

weighed ~0.52 g including harness (range = 0.50 - 0.55 g; n = 24), which

corresponded to 4.2% of the bird’s body mass on average (range = 3.9 - 4.6%; n =

24).

We retrieved geolocators by capturing individuals at their nest box or using

mist nests in 2014 (n = 26) and 2015 (n = 3). Geolocation data were downloaded

and, if still recording data upon recapture, linearly corrected for clock-drift (max =

85 sec). We determined twilight times with TransEdit (British Antarctic Survey,

Cambridge) on transformed light data (i.e., log (Lux)* 20) with thresholds between

6-12, and a minimum dark-period of four hours. We used a Loess-function in the

R-package GeoLight 1.03 (Lisovski & Hahn 2012) to remove clear outliers from

the transition file. We used geolocator-specific k-values to define when points are

outliers (range: 2-3), because data quality varied among loggers. Per geolocator,

we tried various k-values and chose the value that excluded most late sunrises and

African departure determines variation in spring arrival

139

early sunsets (i.e. points influenced by shading), without filtering out many early

sunrises and late sunsets.

Timing and duration of migration

Filtered transition files were used to define timing of major migratory events: i.e.,

onset of autumn migration, arrival at the stationary non-breeding area, onset of

spring migration, and arrival at the breeding grounds. Migration schedules were

not inferred at a finer scale to prevent that differences in the number of ‘stopovers’

is just due to data quality (within and between birds) rather than movement

behaviour. The breeding period in Europe and non-breeding residency period in

sub-Saharan Africa (hereafter, ‘wintering’) could also include smaller-scale

(especially latitudinal) movements, but not large-scale directional movements such

as during autumn and spring migration (figure S4, Supplementary material). To

extract timing events, we used the ChangeLight function in the R-package

GeoLight (Lisovski & Hahn 2012), which marks transitions between stationary and

movement periods based on the quantile probability threshold ‘Q’ and a minimum

stopover of 3 days. We used geolocator-specific Q-values (ranged:0.88-0.95)

because the interpretation of Q is influenced by data quality. We chose Q-values

that picked up more changes than we needed (between 10-18 periods) to increase

our ability to extract movements during periods when shading events were

dominant.

In several cases, single outliers were thereby regularly erroneously defined as

movements. Therefore, short periods as defined by ChangeLight were manually

pooled into four major phases, based on position-overlap of periods (plotted with

preliminary sun elevation angles obtained by Hill-Ekstrom calibration) and

directional changes in twilight times, longitude and latitude. Gradual movements

can be difficult to detect with the ChangeLight-function during periods when data

quality is low (Ouwehand et al. 2016), possibly because day-day shading exceeds

the distance of daily movements. We therefore compared the migration schedule

inferred from ChangeLight with visual inspections from longitudes over time.

Three of 26 spring events, i.e. winter departure or breeding arrival, were not

recognized: with differences of more than two weeks from the closest event

inferred via ChangeLight. In autumn, deviations in breeding departure or wintering

arrival were more common: i.e. 5 days or more for 13 of 54 events (of which n = 3;

range 10-15d). In such cases the particular movement event (i.e. the one not

recognized by ChangeLight) was adjusted based on visual inspection of directional

changes in twilight times and longitude. Our method is thus not fully-standardized,

but strong resemblance between geolocation and field estimates of spring arrival

suggests a high accuracy of our approach to define timing events, at least in spring

(Both, Bijlsma & Ouwehand 2016). Although autumn schedules may seem less

accurate (by gradual movements, stronger shading), the clearer longitudinal

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component in autumn migration compared to spring helps to reliably infer autumn

migration events (figure S4, Supplementary material; Ouwehand et al. 2016).

Two other migratory events could be inferred with high exactness from our raw

light-data files: twice a year a short period occurred with smooth transitions

without shading events and high maximum daily light values. Such events lasted 1 -

2 day-light curves, and suddenly ended with an abrupt occurrence of shading

during the day. Such bright periods refer to short windows of diurnal-flight

(hereafter, ‘diurnal-flight’) that likely enable individuals to rapidly cross barriers

(Ouwehand & Both 2016). These diurnal-flights periods are associated with large

changes in twilight times and major migratory movements and initiated from major

fuelling sites: i.e. the Iberian peninsula in autumn and the wintering locations in

spring (Ouwehand & Both 2016). Autumn diurnal-flight was detected in all birds

and spring diurnal-flight in 14 of 15 birds where devices worked long enough to

record the onset of spring migration.

Wintering longitude

GeoLight was used to calculate longitude positions (but not latitude) twice each

day. Since flycatchers show site-fidelity to wintering sites (Salewski, Bairlein &

Leisler 2000), we used the median longitude in January to approximate wintering

locations. Using such a core dry-season period reduces effects of shading, and

hence improves longitude precision (Ouwehand et al. 2016). Precision (i.e. 25-

75% quartile range) was on average, 0.50°W and 0.45°E of the median longitude.

As shading conditions can change sharply in flycatchers even within stationary

periods (Ouwehand et al. 2016), obtaining reliable geolocation estimates of

latitude is therefore difficult. Changing shading conditions limit the use of in-

(breeding-)habitat-calibration to obtain appropriate sun elevation angle for the

whole year, challenge the assumption of stable shading to perform Hill -Ekstrom

calibration (see Ouwehand et al. 2016). Moreover, precision of latitude will easily

cover the whole latitude range in winter, and is therefore not used for our within -

population comparison of events.

Statistical analyses

Geolocator impact

We explored potential impact of geolocation deployment and harness type by

comparing local return rates, and spring arrival and laying dates of birds with and

without geolocators (hereafter ‘controls’) for 2013-2014. Controls consisted of

birds that raised chicks in the same study-plots as the geolocator individuals

(except for four individuals in one plot, with less systematic catching and

monitoring). Proper controls for unpaired males were missing, as all unpaired

males were equipped with geolocators. We excluded one female returning without

geolocator from the impact analysis. Local annual return rates were the number

recaptured in 2014 divided by the number within a group in 2013.

African departure determines variation in spring arrival

141

To test if geolocators affected return rates of flycatchers, we used generalized

linear mixed models (GLMMs) with binomial errors and logit-link function in the

R-package ‘lme4’ (Bates & Maechler 2009). We first test whether harness type

(‘LL’, ‘FB’, ‘controls’) affected return rates within breeding males. Because

differences among harness types were found, we did not pool geolocator birds

equipped with different harness types in further analyses. We tested if geolocators

affect return rates of breeding birds using a GLMM with ‘device’ (geolocator with

LL, control) and ‘sex’, and its 2-way interaction.

Moreover, we tested if geolocators impacted spring arrival and female egg

laying in 2014. We only included arrival estimates until 20 May, as arrival

observations after this period were less systematic and likely refer to individuals

that first tried to settle in other areas. We also excluded two egg laying dates that

probably refer to replacement clutches: i.e. being 20 d later than the population

mean date. We compared arrival and laying dates between years within individuals

as both traits are repeatable (Both, Bijlsma & Ouwehand 2016). To account for

year and sex differences in timing, we defined timing as the ‘relative’ difference in

days from the year- and sex-specific mean for the population (from Both, Bijlsma

& Ouwehand 2016). Using linear models (LMs), we added relative timing in 2013

as covariate when testing for geolocator impact on timing only if it significantly

explained relative timing in 2014. This was true for male arrival and when arrival

timing of sexes was pooled, while ‘relative timing 2013’ dropped from the model

when testing females separately.

Timing of annual cycle events

We first analyzed how timing between consecutive migratory events were

correlated using LM (i.e. from breeding departure until spring arrival). Because we

aimed to understand which event was most important to explain variation in arrival

dates, we tested the strength of correlations between timing of spring arrival and

any of the preceding timing stages. If no geolocation estimate was available for

spring arrival timing (i.e. n=15 geolocators stopped working before birds arrived at

their breeding grounds), we used field estimates of arrival instead, as these two

measures are highly correlated (i.e., r = 0.98, n = 13; Both, Bijlsma & Ouwehand

2016).

We hypothesized that variation in migration timing decreases chronological

towards the breeding season and tested this by examining the rank order of

temporal variation (i.e., standard deviation of a stage) across migration stages. We

excluded ‘spring diurnal-flight’ as it appears to define the same event as spring

departure time (see results), which agrees with strong resemblance in longitudes

from where birds initiated these events (Ouwehand & Both 2016). The onset of

diurnal-flight (inferred from raw data) thereby confirms the accuracy of our

approach, at least to infer spring departure. We used all 14 individuals with

complete data for the five remaining stages. As we had the hypothesis of reduced

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variation towards the breeding season we used one-tailed Spearman-rank

correlations in the R-package ‘pvrank’ (Amerise, Marozzi & Tarsitano 2015) to

test if observed ranks followed the expected negative rank-correlation, using

conservative p-values. We expected variation in timing partly to arise from sex

differences, and thus repeated the above analyses with an individual’s timing

expressed relative to its sex-specific mean for each event, in case we detected a

trend or significant sex-differences in timing (see Table S2, Supplementary

material).

Next, we explored how migration timing during the annual cycle changed

depending on its sex and breeding state. We built a linear mixed-effect model of

relative timing across five migration stages with individual as random effect, using

the 14 individuals with complete data. We examined whether birds in different

sex/breeding categories (i.e. ‘unpaired male’, ‘breeding female’ or ‘breeding

male’) showed different changes in relative timing (dependent): i.e. the difference

of an individual to the mean date (1 = 1 April 2013) per stage for the entire

population. We examined the main effect of ‘sex/breeding category’ and the

‘sex/breeding category’x‘stage mean date’- interaction and evaluated their

significance using likelihood ratio tests against reduced models (with maximum

likelihood). This interaction allowed us to test if time schedules of birds from the

different breeding categories progress in different ways i.e. become earlier or later

over the year relative to the overall mean date. Parameter estimates were obtained

from minimal adequate models with restricted maximum likelihood. If differences

among sex/breeding categories were found, we post hoc determined how big these

differences were within each stage using LMs.

For breeding birds, we tested whether hatching date of their clutch affected the

timing of subsequent migration events using LMs. However, returning geolocator

females hatched their broods in 2013 earlier than geolocator males (Table S4,

Supplementary material), possibly as a result of non-random return of, mainly

early, geolocator females (figure S2, Supplementary material).To prevent merely

reporting sex differences, rather than the influence of egg hatching date per se on

timing, we expressed hatch dates and the (dependent) timing events as days

relative to the sex-specific mean date, if sex-differences occurred (Table S2,

Supplementary material).

Finally, we used LMs to determine if wintering longitude affected spring

departure and arrival, or was affected by hatch date and wintering arrival.

All analyses were performed in R (v. 3.2.2; R Development Core Team).

Timing values are expressed as means ± s.d. unless reported otherwise.

African departure determines variation in spring arrival

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Results

Geolocator impact

In 2014, 27 of the 100 adults returned that were equipped with geolocators in 2013

(one lost its device). Three more birds returned in 2015. Return rates are based on

birds returning in 2014 (Table S1, Supplementary material), and are thus minimal

estimates of local survival rates.

Males deployed with a full-body harness (FB) returned significantly less than

males with leg-loop harness (LL) and controls (𝑋2 = 8.9, d.f. = 2, p = 0.012; 10 %

vs. 34 % and 43 % respectively). We could not detect significant differences

between harness types on the relative spring arrival of breeding males in 2014

(F2,36 = 1.2, p = 0.30: accounting for arrival date 2013). However, FB males

arrived 6.8 d later than controls, while for LL males this difference was negligible

(+0.05 d; figure S1a, Supplementary material). Because FB males had significantly

reduced return rates and arrived almost seven days later, the subsequent analyses

and results exclude the two FB males.

We found no significant difference in return rates of breeding geolocator adults

(males + females) with LL-harness (30%) compared to the 35% observed in

control birds (𝑋2 = 1.3, d.f. = 1, p = 0.26). Females had a lower local return rate

(𝑋2 = 5.0, d.f. = 1, p = 0.025; 27% vs. 40% for males) but including sex, did not

show an effect of carrying a geolocator (‘device + sex’: 𝑋2 = 1.9, d.f. = 1, p =

0.16) nor did the interaction (‘device x sex’: 𝑋2 = 0.1, d.f. = 1, p = 0.75). Return

rates of unpaired LL males were not significantly different from breeding LL

males (𝑋2 = 0.2, d.f. = 1, p = 0.63; 41% vs. 34% returned respectively).

The timing of relative spring arrival in 2014 for LL-geolocator birds that bred

in 2013 was, on average, 2.8 d later, which was not significantly different from

control birds (F1,64 = 2.0, p = 0.16: accounting for arrival date in 2013). This delay

was mainly caused by non-significant differences in females: geolocator females

were 3.2 d later in spring arrival (p = 0.37) and egg laying date (p = 0.26) in 2014

when compared to controls (figure S1a,b, Supplementary material).

We found no evidence that early and late birds responded differently to device

deployment (‘relative date 2013 device’: F1,64 < 0.01, p = 0.98).

Timing of annual cycle stages

Arrival date at the breeding grounds was positively correlated with departure from

the wintering grounds (figure 1a). The steep slope and tight correlation

demonstrate that spring migration duration was similar across individuals (mean =

13.6 ± 2.9 d; range = 9-18 d; n = 14), whereas individuals varied in departure date

by up to five weeks (Table S3, Supplementary material). Departure from the

wintering grounds was quickly followed by the onset of ‘diurnal-flight’ (figure

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S3b, Supplementary material), which suggest that most birds almost immediately

started with prolonged flights to cross the Sahara desert.

Post-breeding stages were positively correlated (figure 1c-e): later departing

individuals from the breeding grounds showed later onset of diurnal-flight in fall

and subsequently arrived later at their wintering grounds. We found a tendency for

individuals with later hatching offspring to have a later diurnal-flight onset in

autumn (p = 0.084), but none of the other subsequent stages were correlated with

hatching date (Table S4, Supplementary material). Yet, birds that departed late

from the breeding grounds advanced their time schedules somewhat over the

course of autumn migration (figure 1c). Migration in autumn took 34.3 ± 7.1 d;

range =17-48 d; n = 27), more than twice as long and was more variable than in

spring, as indicated by less tight correlations in autumn (figure 1c,a).

We found no correlation between winter arrival and spring departure (figure

1b), nor between breeding arrival and autumn migratory events such as wintering

arrival (F1,21 = 2.2 , p = 0.15), autumn diurnal-flight (F1,21 = 0.1, p = 0.76) or

autumn departure (F1,21 = 0.2, p = 0.67).

Figure 1. Correlations between timing of consecutive migration events of pied flycatchers over

2013-2014 (breeding males in squares, unpaired males in stars, females in dots), as inferred from

geolocation and/or field data. Solid lines show significant relations (p < 0.05).

African departure determines variation in spring arrival

145

Variation in autumn timing thus disappeared during the half year they stayed in

sub-Saharan Africa (216 ± 11 d, range = 194-231 d). Despite this buffering in

winter, variation in spring departure was large, up to five weeks. The temporal

variation in a stage did not diminish as birds approached the breeding grounds

(Spearman-rank test on s.d. : rs = 0.60, n = 5 stages, p = 0.78, n = 14 birds). This

was partly caused by sex differences in spring departure and arrival; with males

migrated two weeks earlier than females (see Table S2, Supplementary material).

However, even if we expressed timing relative to the sex-specific mean in each

stage, temporal variation did not decrease when approaching the breeding grounds

(Spearman-rank test on s.d.: rs = 0.50, n = 5, p = 0.74): the range in spring

departure dates was still three weeks.

Breeding status and sex influenced how an individual’s relative timing changed

over the season (figure 2), as shown by the interaction of ‘sex/breeding category x

stage mean date’ in the set of individuals for which we had timing across all

migration stages (LMM: 𝑋2 = 14.1, d.f. = 2, p < 0.001, marginal R2 = 0.51). A post

hoc analysis revealed that breeding males were about 10 days later than unpaired

males in departure from the breeding grounds (F1,9 = 44.1, p < 0.0001, R2 = 0.81),

autumn diurnal-flight (F1,9 = 18.9, p < 0.005, R2 = 0.64) and arrival at the wintering

site (F1,9 = 5.7, p < 0.05, R2 = 0.32).

Figure 2. Changes in timing across the annual cycle for breeding females (dots, solid line; n = 3),

breeding males (squares, solid line, n = 5) and unpaired males (stars, dotted line, n = 6). Relative

timing is the mean group-difference from the stage mean date in all 14 individuals. The x-axis

shows the stage mean date. Lines are inferred from LMM.

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146

In spring, these breeding males left five days ahead of unpaired males (a non-

significant difference, p = 0.25) and arrived 4.5 days earlier at the breeding sites

(p = 0.23). Males with breeding duties showed similar departure, diurnal-flight and

arrival at wintering sites in autumn as females (all p > 0.50) but were almost 17

days ahead of females in spring departure and arrival (respectively: F1,6 = 13.6, p <

0.011, R2 = 0.64; F1,6 = 21.6, p < 0.005, R2 = 0.75; figure 2). Spring migration

duration was similar for breeding males, females and unpaired males (F 2,11 = 0.11,

p = 0.90).

Male breeding status did not influence at which longitude an individual spent

the winter (F1,18 = 1.5, p = 0.24), nor were there differences among the sexes (F1,25

= 0.2, p = 0.66). Wintering longitudes ranged from 10.15° W to 5.17° W (mean =

7.4°W ± 1.0°). Within this range, there were no correlations between wintering

longitude and wintering site arrival (F1,25 = 0.1, p = 0.74) nor the onset of autumn

migration (F1,25 = 0.05, p = 0.88). Wintering longitude also did not affect winter

departure (F1,13 = 0.2, p = 0.69) or spring arrival dates (F1,21 = 0.3, p = 0.57) neither

when considering sex differences in spring timing (see Table S2 and S4,

Supplementary material).

Discussion

This paper aims to understand individual variation in timing of the annual cycle in

a long-distance migrant, to elucidate the potential to advance spring arrival and

breeding dates in response to climate change. We found that during spring

migration, pied flycatcher males and females travel in only two weeks from West

Africa to their Dutch breeding sites. Most birds almost immediately started these

migrations with a prolonged flight to cross the Sahara desert. Individuals varied in

departure dates, but not migration duration, resulting in a strong positive

correlation between wintering ground departure and spring arrival. These patterns

were unlikely affected by artifacts of geolocator deployment, as we found no

differences between geolocator birds equipped by leg-loop harnesses with a large

control group. Thus, in our study, variation in spring arrival dates was caused by

variation in departure dates, and not by variation in migration rates.

Annual variation in mean population arrival dates of flycatchers has been

interpreted as variation in migration speed in response to conditions en route,

because of correlations with weather patterns encountered (e.g. Both, Bijlsma &

Visser 2005; Lundberg & Alatalo 1992; Ahola et al. 2004; Hüppop & Winkel

2006). Paradoxically, our data on individuals suggest little potential for Dutch

flycatchers to migrate faster, as they covered >5000 km during spring migration in

less than two weeks, leading to an estimated migration rate of ~370 km/day (i.e.

minimal great-cycle distance / migration duration), which is considerably faster

than similarly sized passerines (e.g. Lemke et al. 2013; Kristensen, Tøttrup &

Thorup 2013; McKinnon et al. 2013, 2014; Hahn et al. 2014).

African departure determines variation in spring arrival

147

Spring departure dates varied over three weeks in males and hence there seems

large potential for selection to advance arrival dates via changes in spring

departure schedules. The population variation in spring departure and arrival was

also not reduced relative to other migration stages, despite the assumed fitness

benefits of properly timed arrival at the breeding sites. As in several other long-

distance migrants (Newton 2008), flycatchers arrived over a considerable period

each spring (Lundberg & Alatalo 1992; Both, Bijlsma & Ouwehand 2016). Our

data support the notion that variation in arrival date is caused by individuals

varying in departure date from their wintering grounds. Similarly strong positive

correlations between winter departure and spring arrival dates were found in great

reed warblers Acrocephalus arundinaceus (Lemke et al. 2013), red-eyed vireos

Vireo olivaceus (Callo, Morton & Stutchbury 2013) and western kingbirds

Tyrannus verticalis (Jahn et al. 2013). In our study, part of the variation in

departure schedules was explained by males departing before females. This fits

with males arriving prior to females in our population (Both, Bijlsma, Ouwehand

2016), but the extent of protandry can vary among populations (Schmaljohann et

al. 2016). Even when taking into account the observed protandry, large variation in

spring departure schedules still occurred.

Variation in wintering ground departure was unrelated to timing events in

autumn, and thus we did not find maintenance in timing differences across the

annual cycle. Instead, the differences in time schedules as found in autumn shifted

relative to timing differences in spring (e.g. in diurnal flight events). The rank-

order in timing among birds broke up during winter, also when sex-differences in

spring timing were accounted for. Such shifts in time schedules were also found

among different barn swallow Hirundo rustica breeding populations (Liechti et al.

2014). We expected consistency if endogenous schedules determine autumn and

spring migration, or if individuals with an early autumn migration and arrival at

their wintering sites, have an advantage later in the annual cycle – e.g. via prior

occupancy – that enables them an earlier departure and arrival in the following

spring. We did not detect correlations that hint at the latter: e.g. wintering

longitude was not correlated to an individual’s arrival at or departure date from the

wintering grounds. Previous studies that found similar patterns, have often

suggested that autumn migration timing is more easily adjusted, while spring

migration is under stronger selection and/or less flexible (Stanley et al. 2012;

Senner et al. 2014; Sergio et al. 2014).

How annual cycles developed across the year depended strongly on whether or

not birds bred. Unpaired males left their territories about 10 days ahead of

breeding males. Despite their earlier winter arrival, most unpaired males started

spring migration later than breeding males and hence arrived later at the breeding

sites. Intriguingly, the probability that a returning geolocator male got mated in

2014 did not depend on their spring arrival timing per se, but rather on their prior

breeding status. Only 17% of the males that were unpaired in 2013 were found

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148

breeding in 2014, while 67% of males that bred in 2013 also bred in 2014. This

hints at intrinsic quality differences in birds that affect both breeding prospects and

migration schedules. Intrinsic differences in spring departure may be dictated by

genetic and photoperiod-induced migratory schedules (Maggini & Bairlein 2012;

Saino et al. 2015; Bazzi et al. 2015), although other factors such as wintering

conditions or age can also influence departure decisions (Kristensen et al. 2013;

Sergio et al. 2014; Cooper, Sherry & Marra 2015; McKinnon et al. 2014; Mitchell

et al. 2015). In our study, male breeding status was also associated with age: five

out of six non-breeders with geolocators were in their second year, whereas only

two out of ten breeders were second year males. Annual cycle schedules are

expected to vary with age: arrival date advances with age up to four years in male

flycatchers (Both, Bijlsma & Ouwehand 2016; Potti 1998). Differences in breeding

prospects and migration schedules between breeders and non-breeders may thus be

an age effect. Such age effects on arrival timing have also been shown in recent

tracking studies, although these are – contrary to our findings – often reflected in

their migration speeds (as e.g. in McKinnon et al. 2014; Sergio et al. 2014;

Schmaljohann et al. 2016; Mitchell et al. 2015).

Whether the variation in spring departure date is flexible or a more innate

individual trait is unknown, but it shows the potential for adjustment of arrival date

through changes in departure. This fits with the 10-d advance in spring recovery

dates of pied flycatchers in North Africa across 1980-2002 (Both 2010). It seems

therefore paradoxical that previous studies did not observe advancements in spring

arrival in Dutch flycatchers breeding in Hoge Veluwe (Both & Visser 2001). One

may argue that the fast migration and tight correlation between winter departure

and spring arrival were not representative. If the spring of 2014 happened to be

highly favorable and lacked adverse conditions en route this may also explain why

pied flycatchers migrated at rates that are among the fastest recorded in smaller

migrants (e.g. Tøttrup et al. 2012b; a; Lemke et al. 2013; Kristensen, Tøttrup &

Thorup 2013; McKinnon et al. 2013, 2014; Hahn et al. 2014). However, the few

spring tracks of Dutch flycatcher males from previous years (Ouwehand et al.

2016) exhibited similar (2013: mean = 14 d, n = 2) or a slightly longer migration

durations (2012: mean = 19.5 d, n = 2) compared to 2014, suggesting that spring

migration in flycatchers is generally fast. Additionally, spring arrival in 2014 in

our study area was not especially early, again suggesting that conditions were not

exceptionally favorable (Both, Bijlsma & Ouwehand 2016).

It is important to note that variation in departure may result from conditions at

the wintering grounds, which can vary in time and space (Saino et al. 2007; Studds

& Marra 2007). Especially wintering latitude is expected to affect rainfall patterns

and hence habitat quality, and unfortunately our data did not allow to investigate

whether this associates with departure date. Pied flycatchers occupy a range of

wintering habitats in a landscape characterized by gradients in rainfall (Morel &

Morel 1992; Salewski et al. 2002b; Salewski, Bairlein & Leisler 2002a; Dowsett

African departure determines variation in spring arrival

149

2010) that create complex spatio-temporal variation in conditions important for

spring fuelling. Annual variation in departure conditions can thus potentially

explain variation in breeding ground arrival, which is in agreement with the

fluctuations in the strength of repeatability in arrival dates amongst sets of years in

our flycatcher population (Both, Bijlsma, Ouwehand 2016).

Dutch pied flycatchers seem to have limited options to adjust their arrival at the

breeding grounds apart from advancing departure date. In contrast, other long-

distance migrants with slower and more variable migration rates may advance

spring arrival by faster migration.The ability of pied flycatchers to advance arrival

dates in response to rapid climate change might be slowed down by years with

harsh circumstances in winter, or by years in which selection against early

departing birds if they encounter deteriorating conditions during spring fueling or

migration. Interestingly, later migrating flycatchers that head to northern Europe

(e.g. Ahola et al. 2004) experience improved temperatures during migration, which

were held responsible for advanced arrival dates at their breeding grounds. So, in

contrast to the birds in our study, they possibly may still have the ability to

increase their migration speed. Thus between pied flycatchers populations, the

means by which these long-distance migrants can successfully track environmental

changes at their breeding grounds may vary. Individual tracking over multiple

years in various populations will help disentangling whether migration timing is

indeed always tight in pied flycatchers, with selective mortality or flexible

departure decisions driving variation in arrival timing across years.

Acknowledgements

We thank J. Samplonius, R.G. Bijlsma, R. Ubels and our students for their

fieldwork contributions, J. Fox for data extraction from geolocators, R.H.G.

Klaassen, M. Versteegh, N.R. Senner and two anonymous referees for valuable

discussions and/or comments on the manuscript. Financial support was provided

by Netherlands Organisation for Scientific Research (NWO-ALW-814.01.010 to

JO/CB; VIDI-NWO-864.06.004 to CB) and KNAW Schure-Beijerink Popping

foundation (to CB). All experiments were approved by law (permit: 5588, by the

ethical committee on animal experiments of the University of Groningen,

Netherlands).

Data Accessibility

Summary data supporting this article and raw geolocation data will be available at

publication in the Movebank DOI system.

Chapter 6

150

SUPPLEMENTARY MATERIAL VI

Figure S1. Timing of (a) spring arrival and (b) egg laying of pied flycatchers in the year prior

and after geolocation, as shown for control birds without geolocators (‘ctrl’, open symbols) and

with geolocators (‘geo’, filled symbols). Dates are expressed relative to the sex specific annual

population mean of unmanipulated birds. Males had geolocators fitted by leg-loop harnesses (LL)

or full-body harnesses (FB), while all females were equipped using LL. The dashed line shows

the x=y-line.

Figure S2. Boxplots showing the timing of (a) arrival, (b) egg laying and (c) egg hatching in

2013 of breeding birds deployed with geolocators in relation to their local return, i.e. they were

not seen (open plots) or returned in 2014 or 2015 (filled plots). Note that for some birds timing in

2013 was unknown.

African departure determines variation in spring arrival

151

Figure S3. Correlations between timing of migratory events of pied flycatchers over 2013-2014.

Timing is inferred from geolocation data and/or field estimates. Breeding males are shown in

squares, unpaired males in stars and females as dots. Solid lines show a significant relation (p <

0.05) in the LM.

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152

Figure S4. Raw twilight times (sunset, sunrise; left) and estimated longitude (right) across 2013-

2014 as obtained by geolocators deployed on pied flycatchers with leg-loop harnesses (n = 27).

Migration phases are shown as dashed symbols, dots refer to the breeding and northern

hemisphere wintering phase. Grey lines indicate the onset of diurnal-flight in autumn and spring

migration. Triangles indicate combined arrival at the breeding site, i.e. inferred from field or

geolocation data (see Table S3).

African departure determines variation in spring arrival

153

Figure S4. – continued

Chapter 6

154

Figure S4. – continued

African departure determines variation in spring arrival

155

Table S1. Initial sample sizes and return percentages of a) geolocator and b) control birds over

2013-2014 as expressed for the different categories of birds. Two more females and one more

unpaired male with geolocators returned in 2015.

Table S2. Sex differences in annual cycle events for a) all available data in that stage, b) a subset

of individuals for which migration timing was available for five migration stages in the annual cycle

( n = 14). Test statistics are inferred from simple LM, with significant sex-effects shown in bold ( p <

0.5) or trends (p < 0.10) in bold, italics. Sample sizes in a) varied among stages due to differences

in data availability (e.g. geolocation failure).

Chapter 6

156

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African departure determines variation in spring arrival

157

Table S4. Summary of LMs describing relations among annual cycle events. If sex

differences or trends (p < 0.10) occurred in timing events (see Table S2), timing was

expressed relative to the sex specific mean of the event(s). Significant correlations (p < 0.5)

are shown in bold, trends (p < 0.10) are depicted in bold, italics.